embeddings-cli 0.8.1

Maps the geometry of meaning produced by embedding models — survey a corpus into a semantic landscape, then inspect its regions, neighborhoods, directions, and gaps, and compare models. Backs a Surveyor agent that runs measured experiments against the space and records an evidence-backed Atlas.

embeddings-cli

embeddings-cli is a tool for studying embedding spaces as systems, not just querying individual vectors.

An individual embedding is difficult to interpret. Its dimensions rarely correspond to clean human concepts, and inspecting the raw numbers tells us very little.

But an embedding space contains larger structures that can be studied:

• semantic regions and clusters;
• neighborhoods and boundaries;
• recurring directions of meaning;
• dense, sparse, and ambiguous areas;
• gaps in what the model represents;
• differences between models, datasets, domains, and versions.

embeddings-cli treats an embedding model as a semantic landscape.

Instead of only asking:

Which documents are closest to this query?

it asks:

What kind of world does this embedding model create?

The CLI can embed a representative corpus, survey the resulting space, identify regions and directions, compare different embedding models, and generate an interpretable map of the model's semantic coverage.

embeddings survey corpus.jsonl
embeddings regions
embeddings neighbors "agentic culture"
embeddings directions discover
embeddings gaps
embeddings compare model-a model-b
embeddings map --html

The goal is not to assign a human-readable meaning to every dimension. That is usually unstable and misleading.

The goal is to understand the geometry of meaning produced by the model:

• what it places together;
• what it keeps apart;
• which distinctions it preserves;
• which distinctions it collapses;
• where concepts form continuous transitions;
• where the representation becomes weak or uncertain.

Status

The survey surface above is implemented and ships today, alongside the agent-first CLI skeleton this project is built on — identity and introspection verbs (whoami, learn, explain, overview, doctor, cli overview), each with --json. See CLI for the full verb table.

The Surveyor agent

embeddings-cli can also serve as the deterministic tool surface for a Surveyor agent.

The Surveyor does not invent an interpretation from a single projection. It performs experiments against the space:

1. samples representative concepts;
2. searches for natural clusters;
3. proposes names for regions;
4. tests those names against independent examples;
5. discovers candidate semantic directions;
6. measures whether those directions generalize;
7. identifies gaps, overlaps, and unstable boundaries;
8. records the evidence behind every conclusion.

The agent may describe a region, but the CLI provides the measurements that make the description inspectable and reproducible.

Outputs

A study can produce an Atlas containing:

• the datasets and models used;
• representative points and neighborhoods;
• hierarchical region maps;
• discovered semantic directions;
• cluster and boundary evidence;
• coverage and density measurements;
• model comparisons;
• known limitations and uncertain interpretations.

The Atlas is not the embedding space itself. It is an evidence-backed interpretation of that space.

Why it matters

Embedding models are often treated as interchangeable infrastructure. They are selected using retrieval benchmarks and then hidden behind a vector database.

But every embedding model defines its own system of similarity and distinction.

Two models may disagree about:

• whether two concepts are related;
• which property of a sentence is most important;
• how domains connect;
• how multilingual meanings align;
• whether a semantic change is gradual or abrupt;
• which concepts are represented precisely and which are compressed together.

embeddings-cli makes those differences visible.

It turns embeddings from an opaque retrieval component into something that can be explored, evaluated, compared, and understood.

Quickstart

uv sync
uv run pytest -n auto              # run the test suite
uv run embeddings whoami           # identity from culture.yaml
uv run embeddings learn            # self-teaching prompt (add --json)
uv run teken cli doctor . --strict # the agent-first rubric gate CI runs

The project is named embeddings-cli; the installed command is embeddings.

CLI

Shipping today:

Verb	What it does
whoami	Report this agent's nick, version, backend, and model from culture.yaml.
learn	Print a structured self-teaching prompt.
explain <path>	Markdown docs for any noun/verb path.
overview	Read-only descriptive snapshot of the agent.
doctor	Check the agent-identity invariants (prompt-file-present, backend-consistency).
cli overview	Describe the CLI surface itself.
survey <corpus>	Embed a JSONL corpus and write the atlas other verbs read (dry-run by default; --apply commits).
regions	Partition an atlas into semantic regions via k-means, with per-region stability.
neighbors <text|--id id>	k nearest atlas items by cosine distance, plus a neighborhood boundary profile.
directions discover	Derive candidate semantic directions from item pairs and test them on held-out pairs.
directions overview	Describe the directions noun and its sub-verbs.
compare	Compare two atlases' k-NN overlap and rank agreement on a shared probe set — never by coordinates.
gaps	Report corpus-coverage gaps, and (with --probe) what active probing shows about the model.
map	PCA-project an atlas to 2-D; --html writes a self-contained map artifact.

All originally planned survey-surface verbs are now implemented and listed above; nothing remains planned on this surface.

Every command supports --json. Results go to stdout, errors/diagnostics to stderr (never mixed). Exit codes: 0 success, 1 user error, 2 environment error, 3+ reserved.

License

Apache 2.0 — see LICENSE.